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Try to 개발자 EthanJ의 성장 로그
scikit-learn Machine Learning K-Means Clustering 사이킷런 머신러닝 K-평균 군집화 본문
CS & DS/scikit-learn Machine Learning
scikit-learn Machine Learning K-Means Clustering 사이킷런 머신러닝 K-평균 군집화
EthanJ 2022. 11. 21. 15:00
scikit-learn Machine Learning K-Means Clustering
사이킷런 머신러닝 K-평균 군집화
In [1]:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
Data Collection¶
In [2]:
file_url = 'https://raw.githubusercontent.com/dev-EthanJ/scikit-learn_Machine_Learning/main/data/example_cluster.csv'
df = pd.read_csv(file_url)
In [3]:
df.head()
Out[3]:
| var_1 | var_2 | |
|---|---|---|
| 0 | 3.264413 | 6.929164 |
| 1 | 0.220814 | 2.251631 |
| 2 | -8.786197 | -8.333582 |
| 3 | -0.008547 | 2.630791 |
| 4 | 4.912903 | 6.888520 |
In [4]:
sns.scatterplot(x='var_1', y='var_2', data=df)
plt.show()
Training Model¶
In [5]:
from sklearn.cluster import KMeans
kmeans_model = KMeans(n_clusters=3, random_state=814)
kmeans_model.fit(df)
pred = kmeans_model.predict(df)
pred
Out[5]:
array([0, 2, 1, 2, 0, 2, 0, 1, 0, 0, 0, 0, 2, 0, 1, 2, 1, 0, 0, 1, 2, 1,
1, 1, 2, 0, 2, 1, 2, 2, 2, 1, 2, 0, 0, 1, 1, 0, 0, 0, 2, 2, 0, 2,
1, 0, 0, 2, 1, 2, 0, 1, 0, 2, 0, 1, 1, 1, 2, 2, 2, 0, 2, 0, 1, 0,
2, 1, 0, 1, 2, 0, 2, 2, 2, 2, 0, 2, 2, 2, 0, 2, 1, 2, 0, 0, 2, 0,
1, 2, 0, 2, 1, 2, 1, 1, 1, 0, 2, 2, 2, 1, 1, 2, 1, 0, 1, 1, 1, 0,
1, 2, 0, 2, 2, 2, 1, 2, 1, 1, 0, 0, 2, 1, 2, 1, 2, 1, 0, 0, 1, 0,
2, 0, 1, 0, 2, 2, 2, 0, 2, 0, 1, 1, 2, 0, 1, 2, 2, 0, 0, 2, 2, 0,
0, 0, 2, 1, 0, 1, 0, 2, 0, 1, 1, 0, 2, 0, 1, 0, 2, 0, 1, 0, 0, 1,
2, 0, 1, 2, 2, 1, 2, 2, 0, 0, 0, 2, 0, 1, 2, 2, 2, 1, 0, 2, 2, 2,
0, 0, 0, 2, 2, 2, 0, 1, 2, 2, 2, 1, 1, 0, 2, 0, 2, 1, 1, 0, 2, 1,
2, 2, 1, 0, 1, 1, 0, 1, 0, 2, 1, 0, 0, 1, 1, 1, 0, 0, 0, 1, 2, 1,
0, 1, 1, 0, 2, 1, 0, 0, 1, 0, 2, 1, 0, 2, 1, 1, 2, 1, 0, 1, 0, 2,
2, 2, 1, 0, 2, 0, 0, 0, 0, 1, 0, 1, 2, 2, 0, 1, 2, 1, 0, 1, 2, 2,
2, 2, 2, 1, 2, 0, 2, 0, 2, 0, 1, 2, 1, 2, 2, 1, 1, 1, 0, 1, 1, 2,
1, 1, 1, 2, 1, 1, 1, 1, 0, 1, 1, 0, 2, 0, 1, 1, 0, 1, 2, 2, 1, 1,
0, 2, 1, 1, 2, 2, 0, 0, 0, 0, 2, 0, 0, 2, 0, 2, 0, 1, 2, 0, 0, 1,
0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 2, 1, 1, 2, 1, 0, 0, 1, 0, 1,
1, 2, 0, 0, 0, 2, 2, 0, 0, 2, 1, 2, 0, 1, 2, 0, 2, 2, 1, 0, 0, 0,
1, 0, 1, 1, 2, 0, 1, 1, 0, 2, 0, 1, 2, 0, 0, 1, 2, 2, 2, 0, 1, 1,
0, 1, 1, 0, 1, 2, 1, 2, 1, 2, 2, 0, 0, 0, 0, 0, 0, 0, 1, 1, 2, 0,
0, 0, 1, 1, 1, 1, 2, 2, 2, 0, 1, 2, 1, 2, 1, 0, 2, 2, 0, 2, 1, 1,
1, 1, 1, 1, 1, 2, 1, 2, 1, 1, 0, 0, 1, 0, 2, 0, 1, 0, 2, 0, 0, 2,
0, 1, 2, 1, 0, 2, 2, 2, 2, 0, 2, 0, 1, 0, 0, 0, 1, 2, 1, 2, 2, 2,
1, 1, 1, 0, 2, 2, 1, 2, 0, 1, 1, 0, 0, 0, 1, 2, 1, 0, 0, 1, 0, 1,
0, 2, 1, 0, 1, 2, 1, 2, 1, 0, 1, 1, 0, 2, 0, 1, 2, 1, 0, 2, 1, 1,
1, 1, 1, 1, 0, 1, 0, 0, 2, 2, 0, 2, 1, 2, 1, 0, 1, 1, 1, 2, 1, 0,
1, 1, 1, 2, 0, 1, 0, 1, 2, 0, 1, 0, 2, 2, 1, 1, 1, 2, 1, 1, 1, 0,
1, 0, 0, 0, 2, 2, 1, 1, 2, 1, 1, 0, 2, 0, 0, 1, 2, 2, 1, 0, 1, 0,
1, 0, 1, 0, 2, 2, 2, 1, 1, 1, 1, 2, 2, 0, 0, 2, 2, 2, 1, 2, 1, 1,
1, 0, 0, 1, 1, 2, 0, 0, 0, 0, 2, 1, 0, 0, 0, 2, 1, 2, 1, 1, 1, 0,
0, 1, 0, 1, 1, 0, 0, 1, 2, 2, 0, 1, 1, 2, 2, 2, 0, 1, 2, 2, 0, 0,
2, 1, 1, 2, 2, 2, 2, 2, 0, 1, 2, 1, 1, 0, 0, 0, 2, 2, 2, 0, 1, 0,
0, 1, 2, 1, 2, 1, 1, 1, 2, 0, 2, 1, 0, 1, 2, 1, 2, 1, 2, 1, 1, 2,
2, 2, 0, 1, 1, 2, 0, 0, 1, 0, 2, 2, 2, 2, 0, 1, 0, 1, 2, 1, 1, 0,
0, 0, 2, 2, 1, 2, 0, 0, 2, 2, 1, 0, 2, 2, 1, 2, 2, 2, 2, 1, 1, 1,
2, 2, 2, 0, 0, 2, 2, 2, 1, 2, 2, 2, 2, 1, 2, 1, 2, 2, 0, 1, 2, 2,
0, 2, 1, 2, 2, 2, 0, 2, 2, 2, 2, 1, 1, 1, 2, 0, 0, 2, 2, 2, 0, 2,
1, 0, 0, 0, 2, 1, 2, 0, 2, 2, 0, 2, 2, 2, 1, 0, 1, 2, 1, 2, 1, 2,
0, 2, 0, 1, 2, 2, 0, 2, 2, 2, 0, 1, 2, 2, 0, 2, 0, 0, 1, 1, 1, 1,
0, 2, 1, 1, 2, 2, 0, 2, 1, 1, 2, 0, 2, 1, 1, 0, 0, 1, 1, 2, 0, 2,
0, 0, 1, 2, 0, 2, 2, 1, 0, 1, 0, 0, 2, 0, 2, 2, 2, 1, 0, 0, 1, 1,
0, 2, 2, 2, 0, 1, 2, 1, 0, 2, 1, 1, 1, 2, 2, 1, 0, 1, 0, 0, 2, 1,
2, 2, 1, 0, 1, 2, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 2, 0, 2, 0, 2, 1,
0, 0, 0, 1, 2, 0, 1, 0, 0, 0, 2, 1, 0, 2, 0, 1, 0, 0, 1, 0, 1, 2,
0, 0, 0, 0, 1, 2, 1, 2, 0, 0, 2, 0, 1, 1, 0, 2, 1, 1, 1, 0, 1, 0,
0, 0, 0, 2, 0, 0, 0, 1, 2, 0], dtype=int32)
In [6]:
df['label'] = pred
In [7]:
sns.scatterplot(x='var_1', y='var_2', data=df, hue='label', palette='rainbow')
plt.show()
inertia (이너셔)
- 각 그룹에서의 중심과 데이터간의 거리에 대한 합
In [8]:
kmeans_model.inertia_
Out[8]:
3090.03323707666
In [9]:
new_kmeans_model = KMeans(n_clusters=500, random_state=814)
new_kmeans_model.fit(df)
new_kmeans_model.inertia_
Out[9]:
6.224084359937416
In [10]:
df['new_label'] = new_kmeans_model.predict(df)
df['new_label']
Out[10]:
0 77
1 338
2 441
3 91
4 342
...
995 89
996 165
997 480
998 364
999 161
Name: new_label, Length: 1000, dtype: int32
In [11]:
sns.scatterplot(x='var_1', y='var_2', data=df,
hue='new_label', palette='rainbow')
plt.show()
엘보우 기법¶
In [12]:
df = pd.read_csv(file_url)
distance = list()
for k in range(2, 10):
k_model = KMeans(n_clusters=k)
k_model.fit(df)
distance.append(k_model.inertia_)
In [13]:
distance
Out[13]:
[9741.810795093243, 3090.03323707666, 2715.4753007952586, 2380.9356122228182, 2063.6116976957096, 1827.914235211486, 1585.7434071368557, 1373.1835070701209]
In [14]:
sns.lineplot(x=range(2, 10), y=distance)
plt.show()
Data Collection¶
In [15]:
file_url = 'https://raw.githubusercontent.com/dev-EthanJ/scikit-learn_Machine_Learning/main/data/customer.csv'
df = pd.read_csv(file_url)
df.head()
Out[15]:
| cc_num | category | amt | |
|---|---|---|---|
| 0 | 2703186189652095 | misc | 4.97 |
| 1 | 630423337322 | grocery | 107.23 |
| 2 | 38859492057661 | entertainment | 220.11 |
| 3 | 3534093764340240 | gas_transport | 45.00 |
| 4 | 375534208663984 | misc | 41.96 |
In [16]:
df.describe(include='all')
Out[16]:
| cc_num | category | amt | |
|---|---|---|---|
| count | 2.404540e+05 | 240454 | 240454.000000 |
| unique | NaN | 11 | NaN |
| top | NaN | shopping | NaN |
| freq | NaN | 40200 | NaN |
| mean | 3.365543e+17 | NaN | 69.758079 |
| std | 1.191447e+18 | NaN | 150.060270 |
| min | 6.049559e+10 | NaN | 1.000000 |
| 25% | 2.131124e+14 | NaN | 9.740000 |
| 50% | 2.720433e+15 | NaN | 47.530000 |
| 75% | 4.642255e+15 | NaN | 82.950000 |
| max | 4.956829e+18 | NaN | 21437.710000 |
In [17]:
df.cc_num.nunique()
Out[17]:
100
Data pre-processing¶
- 고객들을 군집화 → 분류별 소비금액 (100명)
In [18]:
customer_dummies = pd.get_dummies(df, columns=['category'])
customer_dummies.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 240454 entries, 0 to 240453 Data columns (total 13 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 cc_num 240454 non-null int64 1 amt 240454 non-null float64 2 category_entertainment 240454 non-null uint8 3 category_food_dining 240454 non-null uint8 4 category_gas_transport 240454 non-null uint8 5 category_grocery 240454 non-null uint8 6 category_health_fitness 240454 non-null uint8 7 category_home 240454 non-null uint8 8 category_kids_pets 240454 non-null uint8 9 category_misc 240454 non-null uint8 10 category_personal_care 240454 non-null uint8 11 category_shopping 240454 non-null uint8 12 category_travel 240454 non-null uint8 dtypes: float64(1), int64(1), uint8(11) memory usage: 6.2 MB
In [19]:
cate_list = customer_dummies.columns[2:]
customer_dummies.head()
Out[19]:
| cc_num | amt | category_entertainment | category_food_dining | category_gas_transport | category_grocery | category_health_fitness | category_home | category_kids_pets | category_misc | category_personal_care | category_shopping | category_travel | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 2703186189652095 | 4.97 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
| 1 | 630423337322 | 107.23 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 2 | 38859492057661 | 220.11 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 3 | 3534093764340240 | 45.00 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 4 | 375534208663984 | 41.96 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
In [20]:
for col in cate_list:
customer_dummies[col] = customer_dummies[col] * customer_dummies['amt']
customer_dummies
Out[20]:
| cc_num | amt | category_entertainment | category_food_dining | category_gas_transport | category_grocery | category_health_fitness | category_home | category_kids_pets | category_misc | category_personal_care | category_shopping | category_travel | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 2703186189652095 | 4.97 | 0.00 | 0.0 | 0.0 | 0.00 | 0.00 | 0.0 | 0.0 | 4.97 | 0.0 | 0.00 | 0.0 |
| 1 | 630423337322 | 107.23 | 0.00 | 0.0 | 0.0 | 107.23 | 0.00 | 0.0 | 0.0 | 0.00 | 0.0 | 0.00 | 0.0 |
| 2 | 38859492057661 | 220.11 | 220.11 | 0.0 | 0.0 | 0.00 | 0.00 | 0.0 | 0.0 | 0.00 | 0.0 | 0.00 | 0.0 |
| 3 | 3534093764340240 | 45.00 | 0.00 | 0.0 | 45.0 | 0.00 | 0.00 | 0.0 | 0.0 | 0.00 | 0.0 | 0.00 | 0.0 |
| 4 | 375534208663984 | 41.96 | 0.00 | 0.0 | 0.0 | 0.00 | 0.00 | 0.0 | 0.0 | 41.96 | 0.0 | 0.00 | 0.0 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 240449 | 4716561796955522 | 4.93 | 0.00 | 0.0 | 0.0 | 0.00 | 0.00 | 0.0 | 0.0 | 0.00 | 0.0 | 4.93 | 0.0 |
| 240450 | 30074693890476 | 140.12 | 0.00 | 0.0 | 0.0 | 0.00 | 0.00 | 0.0 | 0.0 | 0.00 | 0.0 | 140.12 | 0.0 |
| 240451 | 30404291151692 | 37.79 | 0.00 | 0.0 | 0.0 | 0.00 | 37.79 | 0.0 | 0.0 | 0.00 | 0.0 | 0.00 | 0.0 |
| 240452 | 4922710831011201 | 17.50 | 17.50 | 0.0 | 0.0 | 0.00 | 0.00 | 0.0 | 0.0 | 0.00 | 0.0 | 0.00 | 0.0 |
| 240453 | 213112402583773 | 25.49 | 0.00 | 0.0 | 0.0 | 0.00 | 0.00 | 0.0 | 0.0 | 0.00 | 0.0 | 25.49 | 0.0 |
240454 rows × 13 columns
In [21]:
customer_agg = customer_dummies.groupby('cc_num').sum()
customer_agg.head()
Out[21]:
| amt | category_entertainment | category_food_dining | category_gas_transport | category_grocery | category_health_fitness | category_home | category_kids_pets | category_misc | category_personal_care | category_shopping | category_travel | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| cc_num | ||||||||||||
| 60495593109 | 48149.62 | 3169.74 | 4043.46 | 5076.36 | 7576.58 | 1631.22 | 5458.58 | 4924.97 | 4889.57 | 3135.25 | 8106.36 | 137.53 |
| 571365235126 | 259784.87 | 13864.51 | 9055.51 | 36525.79 | 48812.73 | 13685.60 | 19342.79 | 17726.82 | 32542.61 | 16672.08 | 45994.60 | 5561.83 |
| 571465035400 | 270081.63 | 16162.98 | 15448.75 | 9081.62 | 41643.82 | 15244.03 | 28282.98 | 13737.95 | 15880.82 | 8771.27 | 59454.96 | 46372.45 |
| 630412733309 | 41959.52 | 3212.46 | 1543.16 | 5786.98 | 8416.70 | 3201.04 | 2474.68 | 3648.97 | 3109.83 | 2592.17 | 7586.79 | 386.74 |
| 630423337322 | 244612.73 | 12786.75 | 6905.07 | 36460.40 | 43854.26 | 12136.74 | 21106.87 | 16807.24 | 32400.76 | 15885.32 | 44287.02 | 1982.30 |
In [22]:
customer_agg.info()
<class 'pandas.core.frame.DataFrame'> Int64Index: 100 entries, 60495593109 to 4956828990005111019 Data columns (total 12 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 amt 100 non-null float64 1 category_entertainment 100 non-null float64 2 category_food_dining 100 non-null float64 3 category_gas_transport 100 non-null float64 4 category_grocery 100 non-null float64 5 category_health_fitness 100 non-null float64 6 category_home 100 non-null float64 7 category_kids_pets 100 non-null float64 8 category_misc 100 non-null float64 9 category_personal_care 100 non-null float64 10 category_shopping 100 non-null float64 11 category_travel 100 non-null float64 dtypes: float64(12) memory usage: 10.2 KB
Training Model¶
In [23]:
from sklearn.preprocessing import StandardScaler
scaler = StandardScaler()
scaled = scaler.fit_transform(customer_agg)
scaled
Out[23]:
array([[-1.40232671, -1.13561684, -0.97106905, ..., -1.07025953,
-1.14142374, -0.61993021],
[ 1.07940687, 0.41407488, 0.16887956, ..., 1.80734981,
0.65996745, -0.22258695],
[ 1.20015148, 0.74712739, 1.62296823, ..., 0.12782479,
1.29993842, 2.7668909 ],
...,
[-1.10768846, -0.9265974 , -1.1902022 , ..., -0.97473624,
-0.66458252, 0.19333459],
[-0.94081475, -0.80789527, -1.09326659, ..., -0.89572163,
-0.70809415, 0.95453742],
[ 0.59998262, 0.97669101, 0.61115793, ..., -0.39803373,
-0.02063061, -0.08679376]])
In [24]:
scaled_df = pd.DataFrame(scaled, columns=customer_agg.columns,
index=customer_agg.index)
scaled_df
Out[24]:
| amt | category_entertainment | category_food_dining | category_gas_transport | category_grocery | category_health_fitness | category_home | category_kids_pets | category_misc | category_personal_care | category_shopping | category_travel | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| cc_num | ||||||||||||
| 60495593109 | -1.402327 | -1.135617 | -0.971069 | -1.000180 | -1.118217 | -1.561756 | -1.145051 | -1.276913 | -1.124693 | -1.070260 | -1.141424 | -0.619930 |
| 571365235126 | 1.079407 | 0.414075 | 0.168880 | 2.090013 | 0.767268 | 1.040236 | 0.844969 | 1.127922 | 1.210503 | 1.807350 | 0.659967 | -0.222587 |
| 571465035400 | 1.200151 | 0.747127 | 1.622968 | -0.606627 | 0.439476 | 1.376630 | 2.126364 | 0.378610 | -0.196523 | 0.127825 | 1.299938 | 2.766891 |
| 630412733309 | -1.474915 | -1.129427 | -1.539741 | -0.930355 | -1.079803 | -1.222903 | -1.572733 | -1.516610 | -1.274986 | -1.185705 | -1.166127 | -0.601675 |
| 630423337322 | 0.901491 | 0.257905 | -0.320220 | 2.083588 | 0.540547 | 0.705908 | 1.097814 | 0.955178 | 1.198524 | 1.640103 | 0.578781 | -0.484796 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 4512828414983801773 | 0.965514 | 0.426639 | 0.196184 | 2.086544 | 0.727562 | 0.725503 | 0.940589 | 1.923480 | 1.086851 | 1.810368 | 0.252344 | -0.401038 |
| 4599735407877245080 | -0.839991 | -0.715988 | 0.394918 | -0.398366 | -0.721701 | -1.263462 | -0.576780 | -0.190013 | -0.818620 | -0.286312 | -0.940283 | -0.504166 |
| 4666314527820883145 | -1.107688 | -0.926597 | -1.190202 | -1.342390 | -0.850378 | -1.073294 | -1.011957 | -1.616986 | -1.182972 | -0.974736 | -0.664583 | 0.193335 |
| 4725837176265195730 | -0.940815 | -0.807895 | -1.093267 | -1.342878 | -0.676572 | -1.287731 | -1.305887 | -1.463747 | -1.050452 | -0.895722 | -0.708094 | 0.954537 |
| 4956828990005111019 | 0.599983 | 0.976691 | 0.611158 | 1.174361 | -0.015887 | 1.530824 | 1.103355 | 0.929397 | 1.172695 | -0.398034 | -0.020631 | -0.086794 |
100 rows × 12 columns
In [25]:
distance = list()
for k in range(2, 10):
k_model = KMeans(n_clusters=k)
k_model.fit(scaled_df)
labels = k_model.predict(scaled_df)
distance.append(k_model.inertia_)
In [26]:
sns.lineplot(x=range(2, 10), y=distance)
plt.show()
Evaluating Model¶
inertia: 그룹의 중심으로부터 외부 요소간의 거리silhouette_score: cluster 내부의 평균 거리 + 최근접 다른 cluster data의 평균 거리
In [27]:
from sklearn.metrics import silhouette_score
In [28]:
def k_model_score(k):
k_model = KMeans(n_clusters=k)
k_model.fit(scaled_df)
labels = k_model.predict(scaled_df)
return silhouette_score(scaled_df, labels)
In [29]:
silhouette = list()
for k in range(2, 10):
silhouette.append(k_model_score(k))
silhouette
Out[29]:
[0.3717625086081594, 0.37507693866618963, 0.4323712086109757, 0.3773911908220341, 0.39907317775973133, 0.3645825845434313, 0.36887391959996196, 0.37600719399523824]
- 실루엣 계수: 더 높은 값일수록 더 좋은 classifier
In [30]:
sns.lineplot(x=range(2, 10), y=silhouette)
plt.show()
In [31]:
k_model = KMeans(n_clusters=4)
k_model.fit(scaled_df)
labels = k_model.predict(scaled_df)
scaled_df['label'] = labels
scaled_df.head()
Out[31]:
| amt | category_entertainment | category_food_dining | category_gas_transport | category_grocery | category_health_fitness | category_home | category_kids_pets | category_misc | category_personal_care | category_shopping | category_travel | label | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| cc_num | |||||||||||||
| 60495593109 | -1.402327 | -1.135617 | -0.971069 | -1.000180 | -1.118217 | -1.561756 | -1.145051 | -1.276913 | -1.124693 | -1.070260 | -1.141424 | -0.619930 | 0 |
| 571365235126 | 1.079407 | 0.414075 | 0.168880 | 2.090013 | 0.767268 | 1.040236 | 0.844969 | 1.127922 | 1.210503 | 1.807350 | 0.659967 | -0.222587 | 1 |
| 571465035400 | 1.200151 | 0.747127 | 1.622968 | -0.606627 | 0.439476 | 1.376630 | 2.126364 | 0.378610 | -0.196523 | 0.127825 | 1.299938 | 2.766891 | 3 |
| 630412733309 | -1.474915 | -1.129427 | -1.539741 | -0.930355 | -1.079803 | -1.222903 | -1.572733 | -1.516610 | -1.274986 | -1.185705 | -1.166127 | -0.601675 | 0 |
| 630423337322 | 0.901491 | 0.257905 | -0.320220 | 2.083588 | 0.540547 | 0.705908 | 1.097814 | 0.955178 | 1.198524 | 1.640103 | 0.578781 | -0.484796 | 1 |
In [32]:
scaled_df_mean = scaled_df.groupby('label').mean()
scaled_df_mean.head()
Out[32]:
| amt | category_entertainment | category_food_dining | category_gas_transport | category_grocery | category_health_fitness | category_home | category_kids_pets | category_misc | category_personal_care | category_shopping | category_travel | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| label | ||||||||||||
| 0 | -0.858082 | -0.739555 | -0.758078 | -0.601061 | -0.653905 | -0.837854 | -0.832432 | -0.866100 | -0.701383 | -0.760106 | -0.689347 | -0.266604 |
| 1 | 0.311574 | 0.149105 | 0.212407 | 1.025558 | 0.109070 | 0.452339 | 0.369237 | 0.716395 | 0.378376 | 0.470040 | 0.010316 | -0.264410 |
| 2 | 1.908120 | 2.155375 | 1.796299 | -0.490418 | 2.290553 | 1.155923 | 0.943950 | 1.255997 | 2.005393 | 1.756210 | 2.272314 | -0.262740 |
| 3 | 1.000533 | 0.776166 | 1.036432 | -0.749213 | 0.310577 | 1.175961 | 1.794775 | -0.011510 | -0.311453 | -0.093603 | 0.989477 | 3.050970 |
In [33]:
scaled_df_count = scaled_df.groupby('label').count()['amt']
scaled_df_count
Out[33]:
label 0 45 1 37 2 10 3 8 Name: amt, dtype: int64
In [34]:
scaled_df_count = scaled_df_count.rename('count')
scaled_df_count
Out[34]:
label 0 45 1 37 2 10 3 8 Name: count, dtype: int64
In [35]:
scale_df = scaled_df_mean.join(scaled_df_count)
scale_df
Out[35]:
| amt | category_entertainment | category_food_dining | category_gas_transport | category_grocery | category_health_fitness | category_home | category_kids_pets | category_misc | category_personal_care | category_shopping | category_travel | count | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| label | |||||||||||||
| 0 | -0.858082 | -0.739555 | -0.758078 | -0.601061 | -0.653905 | -0.837854 | -0.832432 | -0.866100 | -0.701383 | -0.760106 | -0.689347 | -0.266604 | 45 |
| 1 | 0.311574 | 0.149105 | 0.212407 | 1.025558 | 0.109070 | 0.452339 | 0.369237 | 0.716395 | 0.378376 | 0.470040 | 0.010316 | -0.264410 | 37 |
| 2 | 1.908120 | 2.155375 | 1.796299 | -0.490418 | 2.290553 | 1.155923 | 0.943950 | 1.255997 | 2.005393 | 1.756210 | 2.272314 | -0.262740 | 10 |
| 3 | 1.000533 | 0.776166 | 1.036432 | -0.749213 | 0.310577 | 1.175961 | 1.794775 | -0.011510 | -0.311453 | -0.093603 | 0.989477 | 3.050970 | 8 |
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